Ultra-low dose naltrexone enhances cannabinoid-induced antinociception
Bibliographic record
Abstract
Both opioids and cannabinoids have inhibitory effects at micromolar doses, which are mediated by activated receptors coupling to Gi/o-proteins. Surprisingly, the analgesic effects of opioids are enhanced by ultra-low doses (nanomolar to picomolar) of the opioid antagonist, naltrexone. As opioid and cannabinoid systems interact, this study investigated whether ultra-low dose naltrexone also influences cannabinoid-induced antinociception. Separate groups of Long-Evans rats were tested for antinociception following an injection of vehicle, a sub-maximal dose of the cannabinoid agonist WIN 55 212-2, naltrexone (an ultra-low or a high dose) or a combination of WIN 55 212-2 and naltrexone doses. Tail-flick latencies were recorded for 3 h, at 10-min intervals for the first hour, and at 15-min intervals thereafter. Ultra-low dose naltrexone elevated WIN 55 212-2-induced tail flick thresholds without extending its duration of action. This enhancement was replicated in animals receiving intraperitoneal or intravenous injections. A high dose of naltrexone had no effect on WIN 55 212-2-induced tail flick latencies, but a high dose of the cannabinoid 1 receptor antagonist SR 141716 blocked the elevated tail-flick thresholds produced by WIN 55 212-2+ultra-low dose naltrexone. These data suggest a mechanism of cannabinoid-opioid interaction whereby activated opioid receptors that couple to Gs-proteins may attenuate cannabinoid-induced antinociception and/or motor functioning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".